A SEMI-AUTOMATED APPROACH TO SUPPORT THE ARCHITECT DURING THE GENERATION OF COMPONENT-BASED ENTERPRISE ARCHITECTURES

In today?s volatile business environments, enterprises need to be able to flexibly adapt their information systems and add new functionality quickly. Component-based enterprise architectures promise to help solving these challenges by structuring information systems landscapes into modular business components. However, the derivation of business components from conceptual models still poses research challenges as current methods do not adequately involve the architect and his/her situational preferences. In this paper, we propose an advanced method that facilitates a systematic, reflected derivation of business components. The novel contribution of this paper thereby is to show (i) how the architect can define the derivation of business components from conceptual models as a multi-criteria decision-making problem according to his/her situational preferences and (ii) how the architect can systematically verify the stability of the derivation results. We demonstrate the feasibility of the proposed method by demonstrating its implementation as part of the SeaCoAST tool and applying it to the after-sales processes of a world-wide leading automobile manufacturer

Evaluation of Techniques for Structuring Multi-Criteria Decision Problems

This article seeks to overcome the problem of structuring multi-criteria decision problems in a scientifically valid way. For this purpose, we theoretically and empirically compare two techniques which can be used for the purpose of structuring problem domains: card sorting procedures and statistical web mining. Based on two empirical test cases we assess whether decision structuring is reliable regarding the applied structuring method and whether the resulting hierarchies are valid representations of the decision problem at hand. The results indicate that the two techniques lead to quite different goal-criteria hierarchies and that web mining does not produce useful problem representations. In contrast, card sorting seems to be a valid structuring technique. We explain these results by the fact that card sorting procedures are interpretive techniques which are able to deal with vague concepts (criteria) while web mining, as a purely statistical approach, does not work well with ambiguous concepts

The uses of qualitative data in multimethodology:Developing causal loop diagrams during the coding process

In this research note we describe a method for exploring the creation of causal loop diagrams (CLDs) from the coding trees developed through a grounded theory approach and using computer aided qualitative data analysis software (CAQDAS). The theoretical background to the approach is multimethodology, in line with Minger’s description of paradigm crossing and is appropriately situated within the Appreciate and Analyse phases of PSM intervention. The practical use of this method has been explored and three case studies are presented from the domains of organisational change and entrepreneurial studies. The value of this method is twofold; (i) it has the potential to improve dynamic sensibility in the process of qualitative data analysis, and (ii) it can provide a more rigorous approach to developing CLDs in the formation stage of system dynamics modelling. We propose that the further development of this method requires its implementation within CAQDAS packages so that CLD creation, as a precursor to full system dynamics modelling, is contemporaneous with coding and consistent with a bridging strategy of paradigm crossing

Continuous Improvement Through Knowledge-Guided Analysis in Experience Feedback

Continuous improvement in industrial processes is increasingly a key element of competitiveness for industrial systems. The management of experience feedback in this framework is designed to build, analyze and facilitate the knowledge sharing among problem solving practitioners of an organization in order to improve processes and products achievement. During Problem Solving Processes, the intellectual investment of experts is often considerable and the opportunities for expert knowledge exploitation are numerous: decision making, problem solving under uncertainty, and expert configuration. In this paper, our contribution relates to the structuring of a cognitive experience feedback framework, which allows a flexible exploitation of expert knowledge during Problem Solving Processes and a reuse such collected experience. To that purpose, the proposed approach uses the general principles of root cause analysis for identifying the root causes of problems or events, the conceptual graphs formalism for the semantic conceptualization of the domain vocabulary and the Transferable Belief Model for the fusion of information from different sources. The underlying formal reasoning mechanisms (logic-based semantics) in conceptual graphs enable intelligent information retrieval for the effective exploitation of lessons learned from past projects. An example will illustrate the application of the proposed approach of experience feedback processes formalization in the transport industry sector

Integration of decision support systems to improve decision support performance

Decision support system (DSS) is a well-established research and development area. Traditional isolated, stand-alone DSS has been recently facing new challenges. In order to improve the performance of DSS to meet the challenges, research has been actively carried out to develop integrated decision support systems (IDSS). This paper reviews the current research efforts with regard to the development of IDSS. The focus of the paper is on the integration aspect for IDSS through multiple perspectives, and the technologies that support this integration. More than 100 papers and software systems are discussed. Current research efforts and the development status of IDSS are explained, compared and classified. In addition, future trends and challenges in integration are outlined. The paper concludes that by addressing integration, better support will be provided to decision makers, with the expectation of both better decisions and improved decision making processes

An assembly oriented design framework for product structure engineering and assembly sequence planning

The paper describes a novel framework for an assembly-oriented design (AOD) approach as a new functional product lifecycle management (PLM) strategy, by considering product design and assembly sequence planning phases concurrently. Integration issues of product life cycle into the product development process have received much attention over the last two decades, especially at the detailed design stage. The main objective of the research is to define assembly sequence into preliminary design stages by introducing and applying assembly process knowledge in order to provide an assembly context knowledge to support life-oriented product development process, particularly for product structuring. The proposed framework highlights a novel algorithm based on a mathematical model integrating boundary conditions related to DFA rules, engineering decisions for assembly sequence and the product structure definition. This framework has been implemented in a new system called PEGASUS considered as an AOD module for a PLM system. A case study of applying the framework to a catalytic-converter and diesel particulate filter sub-system, belonging to an exhaust system from an industrial automotive supplier, is introduced to illustrate the efficiency of the proposed AOD methodology

Understanding Validity in Structuring Multi-Criteria Decision Problems

The first steps of multi-criteria decision making (MCDM) are typically the decomposition and structuring of the decision problem at hand. As all subsequent process steps of MCDM are based on the initial structuring of the decision problem, the validity of the structure representing the decision problem is of particular importance for the quality of the decision making process. This paper seeks to further develop our understanding of validity in structuring multi-criteria decisions. For this purpose, we link the structuring of decision problems in MCDM to the theory of chunking, which describes how human cognition structures and perceives environmental information. Based on this, we propose that the validity of models representing multi-criteria decision problems can be assessed by evaluating the degree to which they match the structures formed by chunking. We discuss a preliminary framework of how the match between the cognitive and the MCDM model can be tested. To demonstrate how this framework can be utilized in research practice, we apply it to empirically show that algorithmic, bottom-up structuring of MCDM problems leads to valid goal-criteria hierarchies